Furthermore, with the similarity lower bound L d = 0.3, precision and recall of cross-language retrieval are around 30% and 60%, respectively.

with the similarity lower bound L d = 0.3, at least 1,800 (≈ 6, 073 × 0.5 × 0.6) English articles have relevant Japanese articles in the results of cross-language retrieval.


On the computational side, an ontology learning tool is based on a battery of software programs aimed at extracting and formalising domain knowledge, usually starting from unstructured data.

it is equally important to produce a detailed evaluation of these programs, on a quantitative ground, in order to gain insight on the internal and external contingencies that may affect the result of an ontology learning process.


Table 4 demonstrates rather good performances, however the main problem with semantic relation annotation is the unavailability of an agreed set of conceptual relations, and a sufficiently large and balanced training set.

we need to update the set of used relations whenever we analyse a new domain, and re-run the training phase enriching the training corpus with manually tagged examples from the new domain (as for in Table 2).


Finally, code the continuations is the probability of the 'new' morph s k ).

putting this together, we have the corpus coding cost change for the cost change for resegmenting on p is: Note that all terms are local to the prefix p, its including morphs V p and its continuations S p .


We make use of input that is automatically annotated only up to the phrase level, where the verb dependents are not identified.

a significant disproportion between the number of complements and noncomplements (adjuncts and non-dependents) arises among the candidates (complements being significantly fewer).


As the number of examples of the majority class increases, the more likely it becomes for the nearest neighbor of a complement to be an adjunct.

complements are prone to misclassifications.


The probability of selecting a subtree pair is its number of occurrences in the corpus divided by the number of pairs in the corpus with the same root nodes as it: P (< e s , e t >) = |<es,et>| <us,u t >:r(<us,u t >)=r(<es,e t >) |<us,ut>| The probability of a derivation in DOT is the product of the probabilities of the subtree pairs involved in building that derivation.

the probability of derivation <s Again, a translation can be generated by many different derivations, so the probability of a translation w s ←→w t is the sum of the probabilities of its derivations: Selection of the most probable translation via Monte Carlo sampling involves taking a random sample of derivations and outputting the most frequently occurring translation in the sample.


For example, in (Wu, 1995), the translation examples used for building the translation alignments are selected based on strict constraints.

these approaches indirectly limit their application in using the translation examples that are 'free translation' to the development of examplebased machine translation system.


"barracas" and "praia", of the source sentence do not explicitly appear in target sentence.

in the conventional alignment process, to achieve a fully aligned structural representation for such sentences-pair may be problematic.


The finding of translation units between the sentences pair is relying on structure tree of Portuguese sentence and the sequences of lexical words from Chinese sentence.

instead of analyzing the Chinese sentence in deep, we analyze the Chinese sentence in the lexical level by using the Chinese Lexical Analysis System (ICTCLAS) (Zhang, 2002).


As illustrated in Figure 8, the alignment can be identified only at the level where the number and the part of speech of constituent unit of Portuguese ("[as barracas de praia] NP ") are consistent to that of the lexical item in Chinese sentence ("[更衣室] N ").

the correspondences between the associated structure of Portuguese sentence and the translation fragments of Chinese sentence can be determined and established.


However it is well known that dictionaries contain only a small part of multiwords actually used in language.

there is still wide room to improve KNOWA's capability to handle multiwords.


The solution is based on the observation that content words tend to be less involved in multiple potential correspondences than function words, and that function words tend to be attached to content words.

the basic idea amounts to trying first the alignment of content words, and only in a second phase trying the alignment of function words relative to the position of content words to which they are attached.


Note that GIZA++ cannot be tested on a test set distinct from the training set.

we trained GIZA++ on the whole EuroCor corpus, including the file in the test set.


Whereas in the Hansard corpus the texts are direct translations of each other, in the EuroCor corpus it happens quite frequently that the English and Italian texts are both translation of a text in a third language.

the texts are much more difficult to align.


One is an ASR error, which is basically inevitable in speech communications.

an adequate confirmation is indispensable in spoken dialogue systems to eliminate the misunderstandings caused by ASR errors.


On the other hand, if a critical error occurs in the ASR result, such as those in the product name in software support, the following retrieval would make no sense.

we also introduce a confirmation prior to the retrieval for critical words.


Because it is difficult to define discourse relationships between sentences, depending on the actual application, it is usually enough to define and detect the dependency structure of each sentence.

the CSJ was annotated with intra-sentential dependency structures for sentences in the same way this is usually done for a written text corpus.


The biggest problem in dependency structure analysis with spontaneous and long speeches is that sentence boundaries are ambiguous.

sentence boundaries should be detected before or during dependency structure analysis in order to obtain the dependency structure of each sentence.


For example, " (kore-ga, this)" depends on " (tadashii-to, is right)" and " (watashi-wa, I)" depends on " (omou, think)" in the sentence " ", where " " denotes a bunsetsu boundary.


We compare the results with a set of adjectives classified by human judges according to semantic characteristics.

we intend to induce semantic properties from syntactic distribution.


The morphological mapping works best for nonderived adjectives: 14 out of 16 were basic in denotation (the remaining two were classified as object).

our hypothesis seems to be backed up by the data available.


Manual ontology merging using conventional editing tools without intelligent support is difficult, labor intensive and error prone.

several systems and frameworks for supporting the knowledge engineer in the ontology merging task have recently been proposed (Noy and Musen 2000).


In fact, the lexicon similarity is applied to measure the conceptual similarity.

the lexicon similarity of set can be defined as the following equation: ( , ) Words defined in the and Words defined in the or the evaluation of the non-taxonomic relation is defined as


They are ordered by decreasing score, where the score can indicate the average score of the objects in competition, or the inverse of the average number of candidates per object in competition.

the expert can choose to resolve first underspecifications that contain likely objects, or underspecifications that involve few candidates so that the validation of an object will prune more candidates from the compact representations.


Then, Once the matrix X is obtained, we perform its singular value decomposition (SVD) and approximate it by keeping the largest R singular values and setting the rest to zero.

where, U(IJ × R) and V(K × R) are orthonormal matrices and S(R×R) is a diagonal matrix.


They each play an important role in deciding the semantics associated with the whole sentence or document.


This improvement in the performance is attributed to the finer level of syntactic information available now in the form of supertag.

given the supertag, the choice of the word becomes very limited and thus perplexity decreases.


The decrease in perplexity across the SVD dimension shows that the SVD also plays an important role and thus for SELSA it is truely a latent syntactic-semantic analysis.

if we devise an algorithm to predict the supertag of the next word with a very high accuracy, then there is a gurantee of performance improvement by this model compared to LSA.


Our next experiment, was based on no knowledge of content or function word type of the next word.

the LSA and SELSA matrices had all the words in the vocabulary.


Apart from this, there were 152, 145 and 147 supertags anchoring function word only, content word only and both types of words respectively.

given a supertag belonging to function word specific supertags, the 'vocabulary' for the target word is reduced by orders of magnitude compared to the case for content word specific supertags.


Because we used N -best recognition hypotheses, the log-linear model chose the recognition hypothesis among the N hypotheses which yielded the best translation.

speech recognition could be improved if the higher accurate recognition hypotheses was chosen for translation.


First, it is inherent in the notions anaphor and antecedent that the antecedent is the source of interpretation for the anaphor.

if an anaphor's antecedent is identified and interpreted, the anaphor is supposed to be interpretable as well.


As for its antecedent, it makes more sense to say that it has the witch in this play as its antecedent than the subject phrase I. I is not anaphoric to the expression the witch in this play, as the interpretation of I is independent it.

if one assumes that the subject phrase I is the antecedent of the predicative phrase the witch in (3b), then one does not get access to the desired information that the predicative phrase the witch refers to the same witch as does the prior phrase the witch in this play.


2003), with limited coverage, since only few attributes typically apply to all intended referents and to none of the potential distractors.

van Deemter (2002) has extended the set of descriptors to boolean combinations of attributes, including negations.


The monad-granularity is "Word", i.e., each monad corresponds to one monad.

the word with id 10001 consists of the monad set {1}.


We have made the evaluation on dictionary entries because they are already part of the net-  Table 4: Results work of senses, to avoid raising other issues too early.

we are not exactly in the context of disambiguating free text.


If a case slot of <obligatory case> by SBA or <other> is not merged into case slots in definition sentences, it can be considered that it indicates a meaning of N h which is not covered in the dictionary.

such a case slot constitutes an independent case frame.


The reason why they are remaining is that they are not always described in the definition sentences, but their frequent occurrences indicate they are obligatory cases.

we add these case slots to the case frames derived from definition sentences.


For each test noun, possible case frames were considered, and for each case frame, obligatory case slots were given manually.

68 case frames for 65 test nouns were created, and 35 test nouns have no case frames.


To improve the efficiency of the algorithm, the structure can be pruned by the following set of rules, generated from the language analysis: The nodes surrounded by a rectangle in Figure 1 are pruned according to the rules above.


KATAKANA String → Symbol (we), (e) → a (we), (ue) → b (twu), (to), (tsu) → c (macron) → α (small e), (e) → d In (Shishibori and Aoe, 1993) and (Kubota et al., 1993), they only paid attention to applying their replacement or rewrite rules to words themselves and didn't pay attention to their contexts.

they wrongly decide that " " is a variant of " ."


The cosine measure tends to overscore frequently appeared words.

in order to avoid the problem, we treated log(N + 1) as a score of a word appeared in a context.


In the case of " (Ludwig Erhard-1)" and " (Ludwig Erhard-2)", the semantic similarity becomes 0.17 as shown in Table 4.


Through the evaluation, we found that no correct variant pairs appeared from 10 to 12.

we think that treating candidate pairs whose string penalties range from 1 to 12 can cover almost all of correct variant pairs.


However, the precision of Method p&s was 16.2% higher.

f measure of Method p&s improved 6.7% compared to the one of Method p .


For example, in the sentence from Figure 2, "He" is an external argument Noun Phrase, "bent" is a target predicate, and "over her hand" is an external argument Prepositional Phrase.

the syntactic pattern associated with the sentence is [NP-ext, target, PP-ext].


In this context, as we saw with Figure 4, context makes it clear that any response to draw a line underneath must draw the mouth.

unlike in (1a), all the interpretations here have the same qualitative form.


Constraint networks such as (5a-e) provide a uniform venue for describing the various contextual dependencies required to arrive at natural utterance interpretations.

the contextual representation and reasoning outlined in Sections 3 and 4 is realized by a uniform mechanism in FIGLET: specifications of how to reason from context to find solutions to these constraints.


We define a reading miscue event as any instance in which the child inserts, deletes or substitutes a word during oral reading.

each word spoken by the child is associated with an event label (insertion, deletion or substitution) or non-event (i.e., correct word).


To illustrate the process a short example is given.

the target sentence, it was the first day of summer vacation might be spoken by the child (and transcribed) as, it was it was the third day of summer vacation and the recognition hypothesis might state, it it was the first day vacation this transcription would have two insertions and one substitution events.


The main rationale for this type of underspecification is to ensure monotonicity, and thus upwards compatibility of the output of shallow parsing with semantic representations obtained from full syntactic parsing.

copestake's design of RMRS -Robust Minimal Recursion Semantics -provides an important contribution to a novel line of research towards integration of shallow and deep NLP.


The output node sequence is enriched with the phrase-building information from the original parse tree, and is again input to the phrase building and semantics construction rules.

we define a cyclic cascade, where the output of a cascade is fed in as input to the same rules.


In addition to relatively easier portability to other domains and languages the deterministic dependency parsing promises algorithms that are robust and efficient.

an SRL algorithm based on dependency structures is expected to benefit from those properties.


This is exactly the same data used for CoNLL2004 shared task on SRL.

the results can be directly compared to the performance of the systems that used or that will use the same data.


And in a Topic Page, there is also typically a group of named entities, which could correspond to our original query terms or answer target type.

named entities play important roles in semantic expression and should be used to reflect the content of the pages.


Although the syntactic method showed lower precision of the extracted facts (61% vs. 68%), in spite of parsing errors the recall was higher than that of the surface-based method, judging by the number of correctly answered questions (31% vs. 23%).

the syntactic analysis can in fact be considered as another, intensive way of improving the recall of information extraction, in addition to successfully used extensive ways, such as developing larger numbers of surface patterns or increasing the size of the collection.


Given the many possible forms of analytical questions, it would be counter-productive to restrict them to a predefined number of question/answer types.

the formation of an answer in analytical QA should instead be guided by the user's intended interest expressed in the question, as well as through any follow up dialogue with the system.


Nonetheless, an efficient, spontaneous dialogue cannot be designed on a purely functional layer.

here we are primarily interested in the semantic layer, that is, the information exchange and information building effects of a conversation.


Empirically, the supervised KPCA-based model nearly always disambiguates target words of this kind to the most frequent sense.

for this particular subset of test instances, the precision achieved by the KPCA-based model is essentially no higher than the precision achieved by the most-frequent-sense baseline model (which simply always selects the most frequent sense for the target word).


In the semi-supervised KPCA model, training vectors are drawn from both the labeled training corpus and a much larger unlabeled training corpus.

the maximum number of eigenvectors in the supervised KPCA model is the minimum of the number of features and the number of vectors from the labeled training corpus, while the maximum number of eigenvectors for the semi-supervised KPCA model is the minimum of the number of features and total number of vectors from the combined labeled and unlabeled training corpora.


In particular, the collection Chemical-CompoundTypeByChemicalSpecies is known to be a specialization of the collection ExistingStuffType, via the transitive properties of genls.

by virtue of being an instance of ChemicalCompound-TypeByChemicalSpecies, Water is known to be an instance of ExistingStuffType.


In addition, a system only based on the suffixes (just-suffixes) performs marginally better than always selecting the most common case.

morphology alone would not be adequate for this task.


It does not have the semantic class 921:leisure equipment .

we cannot confirm the third link, even though it is correct, and the domain is correctly extracted.


This method relies on WordNet and shows a rather good agreement between its results and human judgments (88% for Pantel and Lin).

our evaluation was done only for English, and more precisely with WordNet 1.7.


Since M = Greek and H = Deity, we can choose M' from any set other than D deity that contains Greek: These differentiation patterns suggest that new compounds can meaningfully be created by yoking the ontological branches of alphabet and deity together.

from D alphabet we can choose M' to be either Hebrew or Roman, leading to the creation of the LMH concepts Hebrew-deity and Roman-deity.


Internal validation verifies a compound on the basis that it could meaningfully exist, while external validation verifies it on the basis that it does actually exist in a large corpus.

if a compound can be validated externally but not internally, it suggests that the concept may by P-Creative.


Even when this is not the case, it is possible that simply recognizing the name of the correct theorem or definition out of a menu does not imply that the student is fully knowledgeable about the actual content of the theorem involved.

it is plausible that asking the students to express the content of these theorems and definitions in their own words as a form of selfexplanation could lead to a deeper level of their understanding.


Experimental evidence has shown that SNoW activations are monotonic with the confidence in the prediction.

it can provide a good source of probability estimation.


Although the predictions of the second-phase classifier can be used directly, the labels of arguments in a sentence often violate some constraints.

we rely on the inference procedure to make the final predictions.


Here, we are concerned with global constraints as well as constraints on the arguments.

the final labeling becomeŝ The filter function used considers the following constraints: 1.


In addition, there really is a coreference relationship since, in example (10) for instance, both LPPs mention the same table and, in fact, the same 'place'.

we have to account for the coreference link between pronoun "y" and a 'place', described by the antecedent LPP.


As a matter of fact, the semantics of question (13) is: {x/se trouve(mon portable, x)} 2 and one element of this set is therefore an object that can be designated by the LPP "à côté de la tasse"/"next to the cup" (which is clearly a different object from the one designated by the NP "la tasse"/"the cup").

we conclude that LPPs introduce individuals that can be viewed as answers to locative questions.


Practically, these functional terms are obtained by applying functions on variables.

a locative preposition corresponds to a function from this language.


4 Using a classifier that returns confidence scores instead of binary decisions is crucial, because the training vectors that correspond to definitions are much fewer than the vectors for non-definitions (3004 vs. 15469 in our dataset of section 4).

the induced classifier is biased towards non-definitions, and, hence, most unseen vectors receive higher confidence scores for the category of non-definitions than for the category of definitions.


Since Model 0 considers not probabilities of words but only their polarities, the two wordlevel classifier equations yield the same results.

model 0 has 8 combinations and models 1 and 2 have 16 each.


Estimates reported in (Kilgarriff and Grefenstette, 2003) show that for Italian the web size in words is 1,845,026,000; while for English and Spanish the web sizes are 76,598,718,000 and 2,658,631,000 respectively.

our method was not able to extract as much information as for the other two languages.


We assume that for a certain type of documents, the mechanism to perform summarization would be the same.

we only need to find one algorithm that links a collection of documents and their corresponding summaries.


This component of the program is also designed to be reversible, that is, it can produce the corresponding sentence when given a logical formula, but yet another peculiarity of the language complicates the solution: Turkish word order is (almost) free, which basically means that the sentence constituents can be shuffled around without changing the meaning.

a single semantic formula usually corresponds to several different sentences, even without taking synonymity of words into account.


This approach not only allows for the low-cost development of wide-coverage grammars, but also provides the training data for statistical modeling as a byproduct.

we now have a basis for integrating statistical language modeling with deep linguistic analysis.


We ran the extended Espresso algorithm starting with 25 positive and 4 negative seed relation instances for the action-effect relation and 174 positive and 131 negative seed relations for the action-means relation.

we obtained 9,511 patterns with 22,489 relation instances for action-effect and 14,119 co-occurrence patterns with 13,121 relation instances for action-means after 40 iterations of pattern and instance ranking/selection.


Moreover, a word sense may belong to a single verb only while a VN class contains many verbs.

the performance on a certain lemma may benefit from training instances of other lemmas.


The main benefit of this assumption is that the integral of (4) can be obtained in closed form.

p(d|α) (using the standard α notation for Dirichlet parameters) has distribution: Maximum likelihood estimates for the α are difficult to obtain, since the likelihood for α is a function which must be maximised for all components simultaneously, leading some authors to use approximate distributions to improve the tractability of maximum likelihood estimation (Elkan, 2006).


Under the Dirichlet, there is a functional dependence between the expected value of θ j , µ j and its variance, where the relationship is regulated by the constant λ.

two words whose µ j are the same will also have the same variance in the θ j .


(This is based on the combinatorics: the number of possible term-to-term alignments increases approximately quadratically with the number of terms per text chunk.)


But web-based validation has its limitations: it cannot account for novel and creative compounds, nor can it account for conventional compounds whose meaning is not echoed in an expanded paraphrase-form on the web.

we also consider an alternate validation procedure for those paraphrases that can be generated both from a modifier noun relational possibility and from a head noun relational possibility.


tions than the best choice found by the decoder.

the performance of a two-pass SMT system can be improved from two aspects, i.e.


Similarly, the information on length l can also be used to help parse the words whose distance is longer than l. Figure 2 shows that the dependency parser performs best at tagging the relations between adjacent words.

we expect that dependencies of adjacent words in auto-parsed target data can provide useful information for parsing words whose distances are longer than 1.


A named-entity can be classified to a semantic category such as "DNA", "RNA", "Protein" and so on 3 .

we use a set of features to record the category pair of an anaphor and a candidate antecedent.


For example, in the GENIA corpus we found that in some cases an expression of a protein name actually denotes the gene that encodes the protein.

for a given anaphor and a candidate under consideration, it is necessary to record the pair-wise semantic groups, instead of using a single feature indicating whether two expressions are of the same group.


In our experiment, we also interested to compare the utility of PubMed with other general data sets.

we tested pattern mining by us-ing the Google-5-grams corpus 5 which lists the hit number of all the queries of five words or less in the Web.


The bad performance is due to the fact that most of bio-medical names are quite long (2~4 words) and occur infrequently in the nontechnique data set.

a query formed by a biomedical seed pair usually cannot be found in the Web corpus (We found the coverage of the auto-mined patterns mined from the corpus is only about 20%).


A to-kenization is a candidate if it exhaustively parses the entire string and has no overlapping tokens.

for 'comingle', co+mingle, coming+le, comingle, c+o+m+i+n+g+l+e, etc., are some of the candidates, but comi+ngl and com+mingle are not.


In fact, professional abstractors tend to use these operations to transform selected sentences from an article into the corresponding summary sentences (Jing, 2000).

in this paper we consider sentence compression from a more general perspective and generate abstracts rather than extracts.


This can be achieved by extending the decoding algorithm such that the chart cells also store the loss parameters (e.g., for precision, the number of true and false positives (Joachims, 2005)).

this extension leads to a considerably higher time and space complexity compared to decoding.


While the linguistic questions evaluation scores are manually generated we combine them with the automatic methods of ROUGE in an attempt see to what extent these non-content scores can better model both content and overall responsiveness.

in all we consider 9 variables to predict responsiveness.


Inspired by the work of (Leacock et al., 1998), TSWEB was constructed using monosemous relatives from WN (synonyms, hypernyms, direct and indirect hyponyms, and siblings), querying Google and retrieving up to one thousand snippets per query (that is, a word sense), extracting the salient words with distinctive frequency using TFIDF.

tSWEB consist of large ordered lists of words with weights associated to the polysemous nouns of WN1.6.


Our approach focuses on sentences as units of knowledge to produce concise representations that may be useful both as computational objects and as learning resources to present back to the student.

extractive summarization research also informs our work.


As it turns out, using dependency relations to verbs in all argument positions as features to the SVM resulted roughly in a 10∼15% increase in precision.

in our final experiments we let the DNV and DAV evidence consist of dependencies with four additional postpositions (ha, ga, wo and ni), which are used to indicate topicalization, subject, object and indirect object.


On the other hand, structural parameters are comparatively well-estimated from treebanks since they are not keyed to particular words.

it might be beneficial to use a combination of supervised and unsupervised estimation for lexical parameters, while obtaining syntactic (structural) parameters solely by supervised estimation (i.e.


At the same time, we wish to use as little annotated knowledge as possible.

an orthogonal line of research can involve inducing classes for words which are more general than single categories, i.e., something akin to ambiguity classes (see, e.g., the discussion of ambiguity class guessers in Goldberg et al., 2008).


Most of the cases (55) where SPTO outperforms SO are cases where SPTO knows that a phrase pair contains the desired reordering, but SO does not.

sO has to use an external reordering which brings poorer translation than the internal reordering, because the words are translated individually rather than by a single phrase (37 cases), or it has to reject the desired reordering (18 cases), which also hurts translation, since it does not get the correct word order.


In the learning data, the Danish correspondent to 'however' is most often not topicalized, and the subject  is therefore not forced from the initial position.

the rule states that it should only apply, if 'however' is not included in the left context of the reordering.


However, using techniques such as supertagging, the results presented here yield an efficient method for the broader problem of parsing sentences.

we can take the size of the input n to be the number of basic categories in the sequent.


Then, for each ATG, we attempt at most one bracketing and adjoinings with at most 2n+1 other entries for which there can be (2n + 1)f (n) ATGs.

each entry can be processed in worst case time O(n 3 f (n) 2 ).


For example, a maximal path for S 1 and S 2 is P = e → g. An entry in the chart induces two such partitions, one at the left edge of the entry and one at the right edge.

we obtain two such maximal paths and for any ATG G in this entry and any vertex v not in or adjacent to one of these paths, either v is not in G or v has the same neighbourhood in G as it has in the base ATG.


As mentioned in Section 1, the aim of the studies reviewed here is to collect paraphrase knowledge.

they need not to take the grammaticality of expressions into account.


As bunsetsu can be quite long, involving more than ten words, regarding it as a node makes the model complex.

we compare the  following two versions of dependency structures whose nodes are smaller than bunsetsu.


Although a topic is not an event, it can be a broader class of event.

it is helpful for retrieving relevant documents, and thus bilingual verb-noun collocations.


This is natural because categories in the hierarchical structures are defined by different human experts.

a hierarchy tends to have some bias in both defining hierarchical structure and classifying documents, and as a result some hierarchies written in one language are coarse-grained, while others written in other languages are fine-grained.


We note that the similarity obtained by each hierarchy does not have a fixed range.

we apply the normalization strategy shown in Eq.


This is because many verb-noun collocations appear in a pair of relevant documents, as can be seen from Figure 1.

we applied sentence-based retrieval to the results obtained by document-based retrieval.


The results obtained by our method depend on the performance of TC.

it will be necessary to examine some semi-supervised learning techniques to improve classification accuracy.


For example, "Hashimoto remarks on fx rates" in Mainichi documents should be classified into Reuters category "Forex markets," but it was classified into "Government".

"U.S. Treasury has no comment on Hashimoto fx remarks" in Reuters category "Forex markets" and the document "Hashimoto" are not retrieved by a single hierarchy approach.


Each review in epinions.com has separate Pros and Cons (which is also the case in most other review sites).

positive and negative opinions are known as they are separated by reviewers.


For example, in the camera domain, "long" is positive in "the battery life is very long" but negative in "it takes a long time to focus".

we should consider both the feature and the opinion word rather than only the opinion word.


However, in Pros and Cons, comparatives are seldom used (entity features are always there).

we need to first convert comparatives to their base forms.


For example, "long" can be used in "long lag", but it can also be used in "long battery life", "long execution time" and many others.


To remedy this shortcoming, XMG allows for explicit node identifications.

in the above case, X can be identified using the constraint C 1 .X = C 2 .X.


For instance, the semantic index s in the tree for runs is unified during substitution with the semantic indices labelling the root nodes of the tree for John.

the semantics of John often runs is (3) {name(j,john),run(r,j),often(r)} SEMFRAG describes a core fragment of French and contains around 6 000 elementary trees.


Moreover, the basic leaf classes (i.e., the most deeply embedded disjuncts and conjuncts in the grammar specification) are defined by inheritance, the inheritance hierarchy encoding the sharing of tree description fragments and/or feature Figure 2: Tree fragments structures between leaf classes.

several thousand trees are specified using only a few hundred classes.


2) will import a projection class and equate the anchor node of the verbal spine with that of the projection skeleton.

the verb projects its index up to the root (modulo the renaming made necessary by the possibility of an adjunction) as illustrated on the right inside of Figure 3.


The snippets play a crucial role in our approach because we expect that they provide the features that characterize the category to which the entity belongs.

it is important to collect a sufficiently large number of snippets to capture the features that allow a fine-grained classification.


The WordNet Domains package (Magnini and Cavaglia 2000) assigns domains to each sense of the WordNet electronic dictionary.

for each domain a relevant list of words can be extracted.


Semantic relatedness between words is important to many NLP tasks, and numerous measures exist which use a variety of resources.

far, such work is confined to measuring similarity between two words (or two texts), and only a handful utilize the web as a corpus.


The WordNet Domains package 2 (Magnini and Cavaglia 2000) assigns domains to each sense of the WordNet electronic dictionary.


From theory alone nothing beyond the telicity/atelicity requirement for the output value would follow, and even this prediction is not strictly borne out, as was argued in the previous paragraph.

the solution has just been given by empirical observation rather than derived through any theoretical systematization.


Here, the preparatory phase is making up the whole inner part of the regular expression.

applying the temporal adverbial gives the description of an event that takes an hour to culminate.


In (24), on the other hand, the activity phase of the situation got spread over the entire real suffix of the regular expression.

the adverbial adds information concerning the temporal boundaries of the whole situation.


The heart of the proposal is the concept of aspectual underspecification and the idea of formally treating the meaning of temporal prepositions as some kind of dynamic, structural presuppositions.

the whole process is triggered by some kind of type conflict, as traditionally assumed, but gets now controlled by the introduction and consequent filling in of a truth-value gap.


As opposed to what is the case in traditional theories of aspect shift, those possible ways of specifying are compositionally determined, i. e. by exclusive recurrence of semantic material already present in the enriched representation, and via application of the standardly assumed rule for cases of presupposition failure.

the semantic process in question is analysed here as entirely language internal.


GFs and "source" GFs subcategorised for by local predicates, but only the dislocated GFs are instantiated in generation.

traces of the source GFs in input f-structures are removed before generation, and non-projective dependencies are transformed into simple projective dependencies.


The situation is quite different from part-of-speech tagging, or even noun-phrase chunking using sequence learning methods.

for this task both the per-token accuracy as well as the segment precision and recall are equally important in gauging performance.


Usually, an LHS may correspond to multiple RHS's in multiple rules.

in statistical machine translation, the rule selection task is to select the correct RHS for an LHS during decoding.


We use the maximum entropy approach to combine various context features, e.g., context words of rules, boundary words of phrases, parts-of-speech (POS) information.

the decoder can use rich context information to perform context-dependent rule selection.


In these models, LHS and RHS can be considered as phrases and reordering types, respectively.

the selection task is to select a reordering type for phrases.


We integrate the MaxEnt RS models into the SMT model during the translation of each source sentence.

the MaxEnt RS models can help the decoder perform context-dependent rule selection during decoding.


In fact, these rules are not reliable since they usually occur only once in the training corpus.

we use this feature to reward the ambiguous source-side.


Then the new feature scores P rs and P rsn can be computed.

the cost of the derivation can be obtained.


Although the translation produced by the MaxEnt RS model is almost the same as Reference2, as the BLEU metric is based on n-gram matching, the translation "What's" cannot match "What is" in Reference2.

the MaxEnt RS model achieves a lower BLEU score.


The difficulty is that there are overwhelmingly more of them, enough to render a bag-of-character assumption unreasonable for Chinese.

a topical model for Chinese should be capable of detecting the boundary between two words, as well as assigning a topic to each word.


To solve the problem associated with the "蓝 牙" (Bluetooth) example, we need to distinguish between the topics of characters and words.

we introduce a new type of topic for words in addition to the topics assigned to characters.


In other words, the structure of the ideal model is not fixed.

we duplicate word topic variables for each character.


Such words will have a very low probability, which is inversely proportional to the number of partitions.

the perplexity of TWC increased from 7.3513 (Z*T=2*2) to 8.9953 (Z*T=10*10), while that of TNG increased from 20.3789 (T=5) to 193.6065 (T=100).


The tournament model learns which candidate is more likely to be the head between two candidates in a one-on-one game in a tournament.

all of those parsing algorithms try to learn the way to give the highest preference to the correct dependent-head pair among all possibilities though in different settings.


In a Chinese couplet, there is generally a direct one-to-one mapping between corresponding words in the FS and SS, respectively.

the i th character/phrase in F is exactly "translated" into the i th character/phrase in S. Based on this rule, the phrase translation probability ) | (  where m is the number of distinct phrases that can be mapped to the phrase The inverted phrase translation model has been proven useful in previous SMT research work (Och and Ney, 2002); so we also include it in our phrase-based SMT model.


For instance, in eight-sentence Tang poetry, the third and fourth sentences and the fifth and sixth sentences form pairs basically satisfying the constraints of Chinese couplets.

these sentence pairs can be used as couplets in our training algorithm.


Last but not the lest, since the current predication relies on the results of prior predications, exact inference by dynamic programming can't be obtained, and then we have to maintain a n-best candidate list at each considering position, which also evokes the potential risk of depressing the parameter tuning procedure.

many theoretically useful features such as higherorder word-or POS-grams can not be utilized efficiently.


'LCD' in (3) and 'reflex' in (4)) represent the main categories of the demanded objects, but they are not distinctive enough to recognize as knowledge of the authors' demands.

the key task of this research was to find ways to markup the headword of a noun phrase that represents the content of a demand in the syntactic parse tree.


This is because these patterns appear in responses to questionnaires but are not suitable for the writing styles used in discussion forums, as mentioned in Section 3.1.

we used the TM+AP method with the minimum pattern set DP 1 as the baseline in this paper rather than the pattern set DP q .


In Section 4.2 we created a gold-standard dataset with human annotations, however, the number of annotation is not enough to fairly compare the several methods due to the sparseness of the demand targets in the original corpus, and it would be very laborious to prepare a larger annotated dataset.

we used an unannotated corpus for the evaluation in this section.


In the definition of the We are mainly interested in the symmetric mea- because of a symmetric positive semi-definite matrix required by kernel methods.

such measures as the skew divergence were excluded from the consideration (Lee, 1999).


We collect dependency relations between nouns from automatic parses of the web corpus.

10.7M unique dependency relations were obtained.


To identify the correct structure, (b), it is essential that both ane (sister) and otouto (brother) are eligible for the accusative words of yonda (invited).

selectional preferences play an important role in coordination disambiguation.


Relation The most important feature to determine whether two clauses are coordinate is a coordination key.

we consider a coordination key, k i , as clause type f i .


Case assignment CA k represents correspondences between the input case components and the case slots shown in Figure 3.

the generative probability of a content part is decomposed as follows: These generative probabilities are estimated from case frames themselves and parsing results of a large web corpus .


As was noted in section 4.1.2, however, our data also suggest a slight modification of Hypotheses 1 and 3, because a preference for narrow scope existed mainly when the Adjective and the second Noun co-occurred very rarely.

we shall use a modified version of Strong Modification Force (SMF): SMF will mean that Adj and Noun2 have zero (rather than below 30%) cooccurrence in the bnc.


Finally, practitioners of the texting language excel in devising acronyms which condense, sometimes in a radical way, multi-word units: this is for instance the case with afair, which stands for as far as I recall.

from a natural language processing (NLP) point of view, these messages contain an abnormally high rate of out-of-vocabulary forms, and the ambiguity of existing word forms is aggravated, two factors that contribute to degrade the performance of natural language processing tools.


This mechanism is illustrated on Figure 1.

this system can bear with agglutinations (i.e.


As mentioned earlier, each module is implemented as a finite state acceptor or transducer; these modules are built and combined using tools from the FSM  and the GRM (Allauzen et al., 2005) toolkit.

the entire normalization process is computed by a weighted transducer (E • P • D • S), which can be optimized off-line as is commonly done in finitestate speech recognition systems .


For instance, to account for the erratic use of accentuated letters in SMS, the most general pronunciation rule for letter e (incidentally, e is the most frequent letter in French) predicts five pronunciations: /@/, /e/, /E/, /÷/, /ø/, plus the possibility of being deleted.

first group verbal forms such as aime ('I or he/he love(s)') yield (at least) six different pronunciations, which result in many more combinations after resegmentation, such as aime ('(I, you, he/she) love(s)'), aimé ('loved'), aimer ('to love'), aime et ('I love and), aime est (I love is) etc.


In the following discussion, we will use labels in the form of <Cat>-<Target>-<CilinFeatSource> to refer to the various testing conditions, where Cat refers to the category type, Target to the originating source of the target words, and Cilin-FeatSource to the source from which the feature vectors for the Cilin words were extracted.

the label Sub-hk-hk means classification of HK target words at the Cilin subclass level, with feature vectors for target words and Cilin words extracted from the HK subcorpus; and the label Head-tw-bj means classification of TW target words at the Cilin semantic head level, with feature vectors for the target words extracted from the TW subcorpus and those for the Cilin words extracted from the BJ subcorpus.


On a pool of 30,000 tokens, peak performance is about 2.5% F -Score better than the baseline; on a 6000 token pool, the difference is only about 1.7%.

once the peak is reached, the AL process should stop, even if the annotation budget is not yet used up.


For positive decisions, the class probability very often is close to 1, for negative decisions, it is close to 0.

the estimator gives very little score for FN (Equation 3) or FP (Equation 2) in most instances, which leads to the high overestimation of performance.


Positive and negative reinforcement are concerned simultaneously in the ranking process.

pNR 2 favors the sentences biased to the sentences that are important in the new collection and meanwhile novel to the sentences in the old collection.


As observed in the previous sections, answer types can be basically predicted by question type.

in this section, we propose to use multi-document summarization (MDS) techniques for summarizing answers according to question type.


Intuitively, longer texts would contain more information.

when comparing the information content, we limit the length of ASA with several levels to do the evaluation.


Under the loosest interpretation of this capability, any subset of words in a sentence can be a phrase.

the number of rules that the model can learn is exponential in sentence length unless strict heuristics are used, which may limit the model's effectiveness.


Each gap character counts as a single word regardless of how many actual words it spans.

phrase aXb consisting of words a and b separated by a gap is three words.


hinges on a classification of implicative and factive operators into nine implication signatures, according to their implications-positive (+), negative (-), or null (•)-in both positive and negative contexts.

refuse has implication signature -/•, because it carries a negative implication in a positive context (refused to dance implies didn't dance), and no implication in a negative context (didn't refuse to dance implies neither danced nor didn't dance).


These rules are shown in red colour in Figure 1.

hUMT contextual rules are necessary for hindi-Urdu transliteration and they can also be implemented as finite-state transducer using XFST.


Translation of the surface-close languages or inter-dialectal translation can be performed by using mainly transliteration and some lexical translations.

hUMT will also provide basis for Cross-Scriptural Transliteration, Cross-scriptural Information Retrieval, Cross-scriptural Application Development, inter-dialectal translation and translation of surface-close languages.


The HUMT system gives 97.12% accuracy for Urdu to Hindi and 97.88% accuracy for Hindi to Urdu.

the HUMT system works with 97.50% accuracy.


Furthermore, we conducted a comparative analysis of the parsing results represented in word-word dependencies extracted from the shallow CFG analyses.

we could identified a weak-point of the HPSG parser that comes from the parser's architecture.


The parsing model assigns a probability to a parse π by a top-down process of considering each constituent c in π and, for each c, first guessing the preterminal of c, t(c) then the lexical head of c, h(c), and then the expansion of c into further constituents e(c).

the probability of a parse is given by the equation where l(c) is the label of c (e.g., whether it is a noun phrase (np), verb phrase, etc.)


If there are not too many new pairs of words in a sentence, there is a decent chance that we can tag these words correctly and bracket them in a reasonable fashion from context.

using these parses as part of the training data improves parsing because should we see these pairs of words together in the future, we will be more likely to connect them together properly.


If we see the same pair of words in testing, we are likely to connect them in the same fashion.

if we count unknown biheads from gold trees, this feature may explain away other improvements: When gold trees contain a bihead found in our self-training data, we will almost always see an improvement.


2 However, if we go through the coordinate ascent procedure without finding a better set of feature weights, then we do not have to perform the last iteration of nbest decoding, because it will necessarily produce the same n-best lists as the previous iteration, as long as the decoder is deterministic.

we can terminate the overall procedure if either we either fail to generate any new hypotheses in n-best decoding, or the optimium set of feature weights does not change in the coordinate ascent phase of training.


First, we prefer that the scores not fall substantially during the random walk.

we establish a baseline value of m = BLEU( w (0) ) − 0.005 (i.e., 1/2 BLEU point below the initial value) and do not allow a step to go below this baseline value.


with n w,x the number of times w appears in x.

the similarity score is CosTFIDF(x, y) = Experiments show that sequential character-based measures catch mainly coreferent pairs of long NE that differ only by a few characters.


However the way words are "approximately matched" does not forbid the matching of a given word in the second entity twice: in this case, normalization is wrong because this word is counted only once in the norm of the second vector.

there is a potential overflow: actually it is not hard to find simple examples where the final score is greater than 1, even if this case is unlikely with real NE and a high threshold θ.


Since all weights are equal to 1, one may show that sim(G) = |S ∩ S ′ | for any G ∈ once(G).

the score function is simply used for normalization, depending on the given measure: for example, score overlap (G) = sim(G) min(n, n ′ ) .


of term frequencies tf(t, S) • tf(t, S ′ ) • n • n ′ .

summing m × m ′ times idf(t)/n • idf(t)/n ′ in sim(G) is equal to tfidf(t, S) • tfidf(t, S ′ ) (normalization is computed in the same way).


Our measure is robust, since it does not rely on any kind of prior knowledge.

it may be easily used, in particular in applications where NE matching is useful but is not the essential task.


We have used UPSID in order to construct PlaNet and PhoNet.

|V L | = 317 (in PlaNet) and |V C | = 541.


Feature Entropy: For an inventory of size N , let there be p f consonants for which a particular feature f (recall that we assume f to be binaryvalued) is present and q f other consonants for which the same is absent.

the probability that a consonant (chosen uniformly at random from this inventory) contains the feature f is p f N and the probability that it does not contain the feature is .


This difference arises due to the fact that in Model II, the choice of a consonant from the set of neighbors is solely degree-dependent, where the relationships between the features are not taken into consideration.

in order to eliminate this problem, we introduce the model using the featurebased kernel in the next section.


Nevertheless, the average feature entropy of the inventories produced (see curve M3 in Figure 3) are more close to that of the real ones now (for quantitative comparison see Table 2).

it turns out that the groups of consonants that largely co-occur in the languages of a linguistic generation are actually driven by the principle of feature economy (see (Clements, 2008;Mukherjee et al.2007a) for details).


But the user might want a more detailed overview of the shootings.

this leads to continuously reformulating the search query to discover all the facets of the event.


We make the usual assumptions that word co-occurences are independent.

the probability of a sentence is the product of the probabilities of its terms.


According to the ACE annotation guidelines 4 event instances are identified in the text by finding event triggers that explicitly mark the occurrence of the event.

each event instance tagged in our datasets have a corresponding trigger that the annotators used to identify it.


For example, terms like "killing", "death" and "murder" are common triggers used to identify the "Die" event type.

we expect that a system that selects sentences containing one or more candidate trigger terms as positive "on-event" sentences for a given event type, would be a suitable baseline for this task.


This is due to the fact that this method assigns the same probability to all unseen terms.

a term like "professor" that may only occur once in the dataset has the same likelihood of occurring in an "on-event" sentence as a term like "kill" that has a very high frequency in the dataset.


In contrast, the Jelinek-Mercer and Absolute Discounting smoothing methods estimate the probability of unseen terms according to a background model built using the entire collection.

the probabilities assigned to unseen words is proportional to their global distribution in the entire corpus.


When we compare both datasets we find that the ACE data is made up of newswire articles, Broadcast news, broadcast conversational texts, weblogs, usenet newsgroup texts and conversational telephone speech that has been transcribed, whereas the IBC corpus consists mainly of newswire articles reporting fatalities during the Iraqi War.

event instances in the ACE data describing the "Die" event type are likely to report fatalities not only from Iraq but also from more diverse contexts and situations.


For broader events like "Transport" and "Attack" where the trigger terms can be harder to predict, it performs quiet poorly.

as part of future work, we hope to investigate ways of automating the creation of these term lists for a specified event type as this proved to be an effective approach to this task.


The effectiveness of parsers based on manually created resources, namely a grammar and a lexicon, rely mostly on the quality of these resources.

increasing the parser coverage and precision usually implies improving these two resources.


Overspecification is usually caused by the difficulty of describing exhaustively all the subcategorization frames of a lemma (optional arguments, polysemy, etc.).

if for a given lemma the most restrictive frames are also the most frequent, some entries can be overspecified and induce such conflicts.


Candidate POS for unknown forms are then proposed by the tagger's guesser and the most likely to be correct are selected by the tagging process itself.

new POS arise for some forms present in the input.


Well, by introducing a totally underspecified wildcard, either the grammar has so many things to say that it is hard to know what to listen to, or it has so many things to think about that it stutters and does not say anything at all.

we refined the wildcard by introduc-ing some data.


For example, in many romance languages, an adjective can be used as a noun and a noun as an adjective.

an inadequate wildcard may perfectly lead to new parses and provide irrelevant corrections.


These kernels map structures in X into a space whose dimensions correspond to substructures of the elements of X .

strings are mapped onto vectors of substring counts, and trees are mapped onto vectors of subtree counts.


Lexical-semantic features, such as the head word, are basically independent of the predicate's part of speech.

the information from (1) that Peter is a COGNIZER can be used directly for the analysis of the occurrence of Peter in (2).


Unfortunately, pure lexical features tend to be sparse: the head word of the last role, event, is unseen in (1), and due to its abstract nature, also difficult to classify through semantic similarity.

it is necessary to consider syntactic features as well.


The citation summary of an article (A), as defined in (Elkiss et al., 2008), is a the set of citing sentences pointing to that article.

this source contains information about A from others' point of view.


Colors (numbers) represent facts and boundaries show the clustering result communities.

starting with the largest community is important to ensure that the system summary first covers the facts that have higher frequencies and therefore higher weights.


However, detailed research (Zhou et al., 2005) shows that it's difficult to extract new effective features to further improve the extraction accuracy.

researchers turn to kernel-based methods, which avoids the burden of feature engineering through computing the similarity of two discrete objects (e.g.


For example, in the sentence "…bought one of town's two meat-packing plants", the condensed information "one of plants" is sufficient to determine "DISC" relationship between the entities "one" [FAC] and "plants" [FAC], while SPT/CS-SPT include the redundant underlined part.

more unnecessary information can be safely removed from SPT/CS-SPT.


However, since our task is to extract the relationship between two named entities, our focus is on how to condense Noun-Phrases (NPs) and other useful constituents for relation extraction.

constituent dependencies can be classified according to constituent types of the CFG rules: (1) Modification within base-NPs: base-NPs mean that they do not directly dominate an NP themselves, unless the dominated NP is a possessive NP.


While PP can be removed from the rule ("VP VBZ PP"), the VP should be kept in the rule ("S NP VP").

the tree span looks more concise and precise for relation extraction.


Typical ways to realise this strategy are unscoped logical forms, neo-Davidsonian semantics, minimal recursion semantics, and similar formalisms.

a sentence like "I want a pepperoni pizza" might be represented as something like Approaches based in the linguistic tradition were dominant about 10 to 15 years ago, when they were used in major systems like Germany's Verbmobil (Wahlster, 2000) and SRI's Spoken Language Translator (Rayner et al., 2000).


..] has been marked with the tag that would be immediately above it in the nested version.

the element [loc, table] is inside the scope of both the obj tag and the on tag; however, the AFF version assigns it the on tag, since this is the innermost one.


Given the fact that functional tags have some claim to universality, it is reasonable to hope that many tags will map onto themselves.

a typical tag rule might map the English subj tag to the Arabic subj tag, which we write as tag_transfer_rule(subj, subj).


In all corpora, more than 95% of the constituents are labeled using less than 10 prominent labels.

we generated two grammars for each corpus we experimented with, one having T labels and the other having P labels.


Each cell in the chart represents a possible constituent spanning a substring, which is identified by the indices of the first and last words of the substring.

the cell identified with i, j will contain possible constituents spanning the substring w i .


There are O(n 2 ) cells associated with cases 1 and 2, each of which has a total amount of work bounded by a constant, hence the total amount of work associated with the cells not in O is also O(n 2 ).

the overall worst-case complexity of the algorithm under these conditions is O(n 2 ).


This is supported by the fact that the inter-annotator agreement level for the identification of events was 0.49 (see Table 4).

in terms of event extraction performance, a precision of 49.0% on 10-fold cross validation is almost comparable to human experts.


However, since the frequency is shown to be a good clue for syntactic and case structure analysis (Kawahara and Kurohashi, 2006), we consider the frequency also can benefit zero pronoun detection.

we propose a probabilistic model for zero anaphora resolution that fully utilizes case frames.


There are three mentions 3 in the first sentence, and the two mentions, hybrid car and Prius, appear in apposition.

after the pre-processes, two entities, {Toyota} and {hybrid-car, Prius}, are created.


In this case, though there is a mention hybrid-car that is not a case component of hatsubai (launch) by itself, it refers to the same entity as Prius refers.

there is no entity that is not linked to hatsubai (launch), and no further analysis is conducted.


However, while A (s j ) = 1 means s j is not filled with input case component but filled with an entity as the result of zero anaphora resolution, case frames are constructed by extracting only the input case component.

the content part of a zero anaphora antecedent n jm is often not included in the case slot examples.


Only 12 of them were correctly detected and assigned to a wrong entity, that is, 60 of them were not even detected.

we can say our recall errors were mainly caused by the low recall of zero pronoun detection.


A threshold of 6 consistently produced optimal or near optimal results for pre-pruning.

pre-pruning with a threshold of 6 was used in the experiments.


a word group level, a phrasal level, and a clausal level.

some amount of constituent structure may overflow its dedicated level, and be sacrificed (for example, prepositional phrase attachment may be left underspecified).


While traditional metrics forgo examining of the interactions entirely, in applying metrics such as Jaccard coefficient, it is not uncommon nowadays to use more than 10,000 features, a number that a typical metric learner is incapable of processing.

we have two options: one is to find the most important features and model the interactions between them, and the other is simply to use a large number of features.


pointed out, there is no one best measure for all applications.

the metrics must be tailored to applications, even to corpora and other settings.


To make the evaluation realistic, we used a different method to create the test set: we paired each query word with each of the 771 remaining words to form the test set.

in each fold, the training set had an equal number of positive and negative pairs, while in the test set, negative pairs outnumbered the positive pairs.


However, there are no morphosyntactic constraints on what targets may be.

same relations may also hold between adjective phrases, verb phrases, and clauses.


There is an alt relation between [t1] and [t2].

we have an opinion frame of type APANalt between O1 and O2.


However, the speaker expresses several other opinions which reinforce his positivity toward the curved shape.

by using the frame information, it is possible to gather more opinions regarding curved shapes for TV remotes.


Compared to the counts of only individual opinions, we see that the numbers in each cell have increased, while maintaining the same pattern of distribution.

in effect we have procured more instances of opinions for the items.


The focus space overlap feature is motivated by our observation that partici-  Table 4: Automatic Detection of Opinion Frames pants refer to an established discourse topic without explicitly referring to it.

we construct a focus space for each sentence containing recently used NP chunks.


It has also been shown that results obtained from supervised methods improve with access to additional labeled data for training (Ng, 1997).

various techniques for automatically generating training data have been developed.


For this specialized text genre, it has been sufficient to limit the notion of topic to mentions of product names and components and their attributes.

topic extraction has been effectively substituted with a lexicon look-up and techniques have focused on how to learn or acquire an appropriate lexicon for the task.


Topic coreference resolution resembles another well-known problem in NLP -noun phrase (NP) coreference resolution.

we adapt a standard machine learning-based approach to NP coreference resolution (Soon et al., 2001;Ng and Cardie, 2002) for our purposes.


Considering the size of WordNet (115,424 synsets in WordNet 2.0), the labeled Micro-WNOp corpus is small.

the question arises whether it is possible to adapt other data sources that provide subjectivity information to our task.


This contrasts, for example, with sentences in newspapers where one clause might express an opinion, whereas other parts of the sentence are objective.

for the rule-based unsupervised algorithm we lemmatized and POS-tagged the glosses in the Micro-WNOp test set.


More specifically, words that we see as objective but with a strong positive or negative association (such as words for crimes) and words that we see as subjective are annotated with the same polarity label in the GI lexicon.

the GI definition of subjectivity does not match ours.


However, we were able to show that at least one of these methods (based on the SL word list) resulted in a classifier that performed on a par with a supervised classifier that used dedicated training data developed for this task (CV).

it is possible to avoid any manual annotation for the subjectivity classification of word senses.


The time cost of the LDCRF model in each iteration is roughly a quadratic increase concerning the increase of the number of hidden states.

though the LDCRF model requires less passes for the convergence, it is practically slower than the CRF model.


No threshold setting mechanism is suggested in the literature for the scores of the different algorithms, especially since rules for different right hand side templates have different score ranges.

we follow common evaluation practice (Lin and Pantel, 2001;Geffet and Dagan, 2005) and test each learned rule-set by taking the top K rules for each seed template, where K ranges from 0 to 100.


The graphs show that symmetric unary approaches substantially increase recall, but dramatically decrease precision already at the top 10 rules.


Our results for the random 5-fold-cross split of the data at the document level are similar to the ones obtained for the models learned on the pre-established training data (top two rows in Table 4).

our initial split of the data was not an 'unfortunate' division.


We make the assumption that the correct documentlevel event ordering, the document's temporal cohesion can be identified by measuring the closure of the document's temporal relations and that orderings that use incorrect relations do not generate good closures.

the relations that generate the best temporal closure will be considered final and will be used to label the test document's eventevent and event-time pairs.


, x i−1 } which in turn were each aligned with all S snippets.

due to the property of transitivity, all concepts contained in a new intersection v i,j that also exist in the partlyconstructed graph would already be directly associated with x i .


At this stage, x 5 contains two uncovered concepts while x 3 and x 4 contain just one each.

x 5 is selected next and its concepts z C and z D are covered.


RGs also differ from phrase-structure grammars in that their RNs are unordered.

linear precedence need not play a role in stating generalizations.


The set-theoretic notion of RNs in RGs abstracts away from surface phenomena crucial for generating phrase-structure trees.

we next turn to modeling how grammatical relations are realized.


We address corpus building situations, where complete annotations to the whole corpus is time consuming and unrealistic.

annotation is done only on crucial part of sentences, or contains unresolved label ambiguities.


However, there are several overlapping segmentation candidates, which are shown by the other boxes, and possible segmentation by the dashed lines.

the decisions on the word segmentation require considering the context, so simple dictionary lookup approach is not appropriate.


Thus, the decisions on the word segmentation require considering the context, so simple dictionary lookup approach is not appropriate.

statistical methods have been successfully used for JWS tasks.


We assume that the amount of training data is not sufficient to ignore the POS ambiguous sentences, or that the POS ambiguous sentences make up a substantial portion of the total training data.

we used a small part (1/10 or 1/5) of the POS unique sentences for training the CRFs and evaluated their performance using other (4/5) POS unique sentences.


Blacklist From an exhaustive set of Wikipedia topic titles was derived a blacklist consisting of entries that had to satisfy four criteria: 1) no more than three words in length; 2) has no closed-class words, such as prepositions or adverbs; 3) must begin with an adjective and end with a noun (determined by WordNet); and 4) does not contain any numerical characters or miscellaneous symbols that are usually not meaningful in English.

each title in the resultant list is literally a short noun phrase with adjectives as premodifiers.


The window may either be a fixed number of words, or the paragraph or document that a word appears in.

words are considered similar if they appear in similar windows (documents).


In this framework, we will adopt the latter, as -from our experience -entropy-based measures tend to work well for natural language.

we want to find the matrices W and H for which the Kullback-Leibler divergence between V and W H (the multiplication of W and H) is the smallest.


Unfortunately, as stated above, Linguistic Profiling and the Support Vector methods are not very amenable to extracting information about the most salient features.

for now, we will have to fall back on the marker-based classification where it is trivial to identify the source language markers which characterize their source languages most strongly.


Noun phrases are involved not only in the NP feature (f3), but also in our features involving subject, direct object, nominal predicate and question focus.

we decided only to lemmatize verbs (for features f6 and f7) in the current version of our system.


These cues appear to be of a general nature, unrelated to the source domain or application.

despite being automatically acquired from one domain specific corpus, these cues should be equally applicable to new corpora, from a different domain and it is this hypothesis we test.


Although the evaluation results are encouraging, supervised learning approach requires much labeled data.

we investigate co-training by combining labeled and unlabeled data.


Each sentence feature has its unique contribution, and combing them would be advantageous.

we investigate combined sentence features for extractive summarization.


In our preliminary experiments, we find ROUGE-1 score of a model summary is 0.422 (without stemming and filtering stop words).

summaries generated by our supervised learning approach received comparable performance with model summaries when evaluated by ROUGE.


The reason is that when people try to write a more concise summary, condensed contents are included in the summaries, which may not use the original contents directly.

the word-overlapping test tool in ROUGE generates lower scores.


And even if such dictionaries are not available, it is easier to compile one than to build a bilingual corpus.

in this paper, we address the problem of domainspecific SMT, where only domain-specific dictionaries and/or monolingual corpora exist.


This constraint can not be checked at this point, since information about inner nodes is not yet available.


This way restricts learning space to only the best BTG trees 2 , and leads to the loss of many useful annotated rules.

we use an alternative way to extract the annotated rules as illustrated below.


Each merging rule involves 3 nodes (A a , A a l l , A ar r ) and each node has 3 linguistic elements (hw, ht, sl).


The annotation scheme "C" is such a scheme with loose constraint and less knowledge.

an ideal annotation scheme should not be too specific or too generic.


Note that for efficiency, we only allow limited modifications to the initial word alignments.

we only use models derived from IBM-1 (instead of IBM-4) for comparing different word segmentations.


In this case, for the hypotheses c + k we consider only one alternative, which is exactly the same as the assignment of segmentation and alignments in the previous state.


the feature weights were λ 1 = 1, λ 2 = 0, λ 3 = 0.

no alignment information needs to be maintained in this case.


freely switch between different tasks without much signaling.

the user might be confused about which task the interface is talking about.


Conversely, during the system's turn of speaking, it can produce an utterance for any of the dialogue tasks.

the system does not take into account the user's cost of task switching.


To alert the player to the picture game, two solid bars flash above and below the player's cards.

the player will know that there is a waiting picture game without taking the attention away from the poker game.


Of the total 180 picture game prompted, 8 were never started by players 1 .


"Wait" is used to mark a discontinuity in the ongoing topic, which is also required by task switching.

people may use these discourse markers to signal switching to a real-time task.


As a baseline, if we blindly assume that all cases of "do you have" are for task switching, we have an accuracy of 58%.

decision tree learning with the three features has 43% relative error reduction over the baseline.


So, many terms are consistent and repeated a lot of times in different documents which accords with the idea of TF-IDF.

tF-IDF performs relatively well because of the high-quality domain corpus.


However, TF-IDF, as a statistics based algorithm suffers from similar problem as others based on statistics.

it does not perform as well as the proposed TCE_DI and TV_LinkA algorithms.


However, the TCE_DI SW algorithm using s context words occur in the domain lexicon than that of other terms.

new terms are actually ranked higher than other terms in TV_ConSem which explains its higher ability to identify new terms in the low range of N TCList .


The proposed approach still performs much better than the reference algorithms when using a general purpose stop word list, which means that the proposed approach can improve performance well even as a completely unsupervised approach without any training.

the results demonstrate that the proposed approach can be applied to different domains easily even without he proposed approach is  This work was done while the first author was working at the Hong Kong Polytechnic University supported by CERG Grant B-Q941 and Central Research Grant: G-U297.


As we will introduce later, the Chinese dependency parser using case structures applies CKY algorithm for decoding.

the following substrings of cp i are also extracted for each head as horizontal Markovization during case pattern construction.


Although only the sentences with less than k (k=30) words from Chinese Gigaword are used as raw corpus to guarantee the accuracy, there still exist some dependency relations with low accuracy in these short sentences because of the non-perfect parsing quality.

we apply a head-modifier (HM) classifier to the parsed sentences from Chinese Gigaword to further extract head-modifier pairs with high quality.


But in the proposed parser, the probability of 开幕/VV generating the case pattern ' [prep, punc, prep] l ' was much lower than that of 开幕/VV generating the case pattern '[prep] l '.

the proposed parser rejected the incorrect dependency that 据/P modified 开 幕/VV and got the correct head of 据/P as 展示 /VV (show) successfully.


Currently, the best performance of Chinese word segmentation has achieved 99.20% on F-score, but the best accuracy of Chinese postagging was 96.89% (Jin and Chen, 2008).

we think pos-tagging is more crucial for applying parser in real task compared with word segmentation.


Although most annotations in OntoNotes are correct, there is still a small (but unknown) fraction of erroneous annotations in the corpus.

some cleanup procedure is necessary to produce a high-quality corpus.


The special key words set For the second problem, we note that there are only three positions where the SQP will be moved to: (1) the beginning of the sentence; (2) just after the rightmost punctuation (",", ";" or ":") before the SQP; (3) or after a regular phrase such as " 请 问 (May I ask)" and " 告 诉 我 (Please tell me)".

we can learn the reordering templates from bilingual corpus 5 .


We think there are two reasons: firstly, there are much more special interrogative sentences in Test 2 than in Test 1, so the sentences of other sentence types in Test 2 are much fewer than that in Test 1.

fewer candidates are found in Test 2 than in Test 1.


It demonstrates that every reordering model has a positive effect on translation.

our reordering model based on the sentence type is quite successful.


The STSSG can be seen as a generalization of Synchronous TSG (STSG) by replacing elementary tree (a single subtree used in STSG) with contiguous tree sequence as the basic translation unit.

most of previous grammars used in SMT can be interpreted as the reduced versions of the STSSG.


Their study finds surprisingly many examples of translational equivalence that could not be analyzed using binary-branching structures without discontinuities.

they claim that the complexity of these patterns in every bitext is higher than suggested in the literature.


The difference is that we implement it based on our STSSG decoder.

in nature the parser is a standard synchronous chart parser but constrained by the rules of the STSSG grammar.


This implies that the complexity of structure divergence between two languages is higher than suggested in literature (Fox, 2002;Galley et al., 2004).

structure divergence is still a big challenge to translational equivalence modeling when using syntactic structure mapping.


Afterwards -during the induction of alignments -only those tree nodes are left intact that take part in the alignments or are necessary for the production of connected trees.

the output is again a parallel treebank with unambiguous phrase-structure trees for each language side.


Unary productions from the original trees, if available, are collapsed to single nodes, preserving all labels.

the aligner will consider a single node -instead of several nodes -for the same lexical span.


The evaluation results from section 3.3 suggest, however, that the margin for improvement here is very small.

we do not expect any improvements here to bring serious boosts in overall performance.


This means that the set of rules has to be adjusted for each particular case.

the methods presented in this paper are the only available ones that can be used to produce a sufficiently large parallel treebank appropriate for use by state-of-the-art statistical MT applications (eg.


When d < 1, both the centroid and some of its nearest neighboring sentences in each cluster will receive the same abstract annotation.

the annotated corpus will contain more than k sentences.


This comparison experiments aim to analyze the accuracy effectiveness of stopping criteria for heterogeneous uncertainty sampling, compared to that for uncertainty sampling shown in Table 1.

we do not provide the results of the "First" method for heterogeneous uncertainty sampling.


So perhaps there are too many borderline cases in the training data here.

we retrained the classifier of Section 6.1, selecting only those sentence pairs  Table 7: Accuracy of Prediction in the extreme range where the difference was more distinct.


Exceptions involve stylistic devices like direct quotation that license discourse subordination.

sDRT predicts that RFC violations for discourse attachments should be much more rare than those for the resolution of anaphors that provide linguistic clues about their antecedents.


It was shown in (Ross, 2003) that a Bayesian estimate for Bernoulli trials using the beta distribution delivers good credibility intervals 5 , importantly, when sample sizes are small, or when occurrence probabilities are close to 0.

we assume that where the parameters for the two beta distributions are set to Prior information related to p(x) and the conditional probability p(x|y) can be incorporated by setting the hyper-parameters of the betadistribtutions.


In this case, the information conveyed by having a complete match with the query word's feature set is lower in the case of A's feature set than in case of B's feature set.

we suggest its use as a basis of our similarity measure, the degree of pointwise entropy of having an estimate of m matches, as Information(m, q, c) = − log(P (matches = m)).


10`q m´i s the number of possible combinations of pivots which the candidate has in common with the query.

,`q m´•`w −q c−m´i s the number of possible different feature sets that the candidate can have such that it shares m common pivots with the query.


The resulting ranking of the translation candidates is automatically evaluated using the gold-standard.

synonyms that are not listed in the gold standard are not recognized, engendering a conservative estimation of the translation accuracy.


From the approximately 9700 sentences in this corpus, 55% of them are labeled as subjective, while the rest are objective.

55% represents the majority baseline on this corpus.


Both of these problems are further explained through the examples below, where we make the simplifying assumption that the words in italics are the only potential carriers of subjective content, and that, without them, their surrounding contexts would be objective.

their association with an either objective or subjective meaning imparts to the entire segment the same labeling upon classification.


Our aim is not to improve on that method, but rather to verify that the results are reliable across a number of languages.

we conduct this experiment in several additional languages, namely French, German and Arabic, and compare the results with those obtained for Spanish and Romanian.


This step generates two vectors where each dimension is comparable between the two vectors.

the cosine between such vectors can be estimated in order to -indirectly-estimate how similar d q and d ′ are.


The problem is worse for cross-language plagiarism.

in our experiments we use two parallel corpora: Software, an en-eu translation memory of software manuals generously supplied by Elhuyar Fundazioa 5 ; and Consumer, a corpus extracted from a consumer oriented magazine that includes articles written in Spanish along with their Basque, Catalan, and Galician translations 6 (Alcázar, 2006).


This is due to the fact that neither the vocabulary nor its corresponding roots keep important relations.

when language pairs have a low syntactical relationship, CL-CNG is not an option.


Nouns occurring in PPs headed by prepositions such as durante ('during'), hasta el final de ('until the end of'), desde el principio de ('from the beginning of'), and similar expressions are considered to be eventive.

occurrence after one of such expressions will be indicative of an event noun.


The positive or negative results of the npattern checking in all the occurrences of a word are stored in an n-dimension vector.

a single vector summarizes all the occurrences of a word (the type) by encoding how many times each cue has been observed.


So, in order to gain precision, we decided to separate the set of words that could be safely taken as correctly classified.


There are 1.2 million potential dependency links in those sentences, 96.5% of which are not present in a gold standard dependency tree.

the maximum achievable Reduction is 96.5%.


This is perhaps because the few cases of false positives are still likely to be main verbs or auxiliaries, and thus still still likely to have few links crossing them.

many of the filtered links are still correct.


However, these methods do not explicitly account for sample similarities, e.g., the samples with a "four star" rating being more similar to "three star" samples than to "one star" samples.

these methods generally do not perform well, while methods which incorporate sample similarity information achieve improved performance (Pang and Lee, 2005).


The reviews for these classes are likely to omit some of the words that appear in the reviews for classes that are relevant to other nodes, in particular in the lower layers of the tree.

an internal node can be trained using a subset of the features that occur in the entire training dataset.


The main goal of the self-annotation task is to reach the highest possible annotation quality.

only complete extracted relations are used for the annotation process while incomplete data are excluded from the training set.


Another main difference to the town-suburb relation is that the river-bodyOfWater relation is often not mentioned in the first sentence (which usually gives a short definition about the the main entity).

the self-annotation method has to deal with the more complex sentences that are common later in the article.


Software developers and researchers are usually unwilling to reduce the quality of their applications.

we have to consider at first methods to improve a parser, which do not involve an accuracy loss, such as faster algorithms, faster implementation of algorithms, parallel algorithms that use several CPU cores, and feature selection that eliminates the features that do not improve accuracy.


The CPU cache can only store a small amount of the data from the hash table.

the memory controller has frequently to transfer data from the main memory into the CPU.


For Catalan and Chinese, the top results obtained transition-based parsers.

the integration of both techniques as in Nivre and McDonald (2008) seems to be very promising.


Prob-lematic from the viewpoint of generation is that this annotation is not always a connected acyclic graph.

in these cases no valid (connected) syntactic tree can be derived.


The most frequent cases of violation of the connectivity principle are not attached adjectival modifiers, determiners, adverbs, and coordinations; sometimes, the verb is not connected with its argument(s).

prior to starting the training procedure, the semantic annotation must be completed: non-connected adjectival modifiers must be annotated as predicates with their syntactic heads as arguments, determiners must be "translated" into quantifiers, detached verbal arguments must be connected with their head, etc.


This procedure works well since nodes that are close in the semantic structure are usually close in the syntactic tree as well.

subtrees that contain those nodes are considered first.


For Spanish, to the best of our knowledge, no linearization experiments have been carried out so far.

we cannot contrast our results with any reference work.


Rather, the best function should be the one that emerges naturally from the data.

we propose an unsupervised method to discover such a function based on the following principle: the context models for a target word generated from two random samples should be similar.


Our approach only uses bag-of-words in a Naïve Bayes classifier.

the performance of our method is sub-optimal.


The metameasure proposed in (Mihalcea et al., 2006), henceforth called mcs, treats questions as bagsof-concepts.

both cos and mcs may miss important structural information.


The new similarity measure qsim f multiplies the semantic similarity between the two focus words with the bag-of-words similarity between the remaining words in the two questions.

the word "movie" in Q 26 will not be compared with the word "movies" in Q 24 , and therefore Q 26 will receive a lower utility score than Q 25 .


When decomposing the questions into focus words, main verbs and the remaining words, we have chosen to multiply the corresponding similarities instead of, for example, summing them.

a close to zero score in each of them would drive the entire similarity to zero.


In other words, clustering and ranking are regarded as two independent processes in these approaches although the cluster-level information has been incorporated into the sentence ranking process.

the ranking performance is inevitably influenced by the clustering result.


While quan directly evaluate the quality of the generated clusters, we are also quite interested in whether the improved clusters quality can further enhance the quality of sentence ranking and thus consequently raise the performance of summarization.

we evaluate the ROUGEs in each iteration as well.


Edges with a degree of two describe pairwise relations.


Second, as previous discussed, the predicate and argument sense disambiguation can help each other.

we can reflect the help with the division and use Markov logic to represent it.


Here, we only consider the noun and verb word sense disambiguation, which cover most of multisense words.

the word sense disambiguation performance means the accuracy of all nouns and verbs in the test set.


Additionally, the performance of word sense disambiguation is higher than that of the most frequent sense significantly (z-test 7 with ρ < 0.01).

the experimental results show that the Markov logic can achieve considerable performances for word sense disambiguation and semantic role labeling on the latest OntoNotes 3.0 corpus.


As mentioned in Section 2, Kanayama and Nasukawa (2006) validated that polar text units with the same polarity tend to appear together to make contexts coherent.

we believe that the signs in PCA's principal eigenvector are effective in partitioning textual units into bipolar groups.


When off-topic blocks are eliminated, the set of non-co-occurring blocks specifically reveals opposing or jointly-absent relationships between entities.

the bipolarization performance improves as α increases.


As both entities in a pair tend to be absent in off-topic blocks, a large α value will lead to overestimation of the correlation between bipolar entities.

the bipolarization accuracy decreases as α increases.


As a small α value makes co-occurrence blocks important, recapstyle documents will overestimate the correlation between bipolar entities.

the bipolarization performance is inferior when α is small.


It is expected that these manually generalized patterns often yield a low-coverage problem.

we extracted features which enable us to automatically capture more emotion-specific constructions.


In other words, both the predicate and the two arguments are optional provided that at least one of them is present.

the fundamental issue in designing a cause detection system is the definition of the span of a cause text.


In our study, this becomes a significant issue as positive class (event anaphoric) is the minority class in pronoun resolution task.

we utilize a random down sampling method to reduce majority class samples to an equivalent level with the minority class samples which is described in (Kubat and Matwin, 1997) and (Estabrooks et al, 2004).


However, most of these features are not useful for our task, as our antecedents are inflectional verbs instead of noun phrases.

we have conducted a study on effectiveness of potential positional, lexical and syntactic features.


These features are purposely engineered to be highly correlated with positive instances.

such kind of features will contribute to a high precision classifier.


However, the hyper-plane is used to separate positive and negative instances during classification process without consideration of the margin.

if an instance falls in the margin, SVM can only decide class label from hyper-plane which may cause misclassification in the margin.


A parse tree that covers a pronoun and its antecedent candidate could provide us much syntactic information related to the pair which is explicitly or implicitly represented in the tree.

by comparing the common sub-structures between two trees we can find out to what degree two trees contain similar syntactic information, which can be done using a convolution tree kernel.


By using each tree structure along, we can only achieve a performance of 44.4% f-score using the minimum-expansion tree.

we will further investigate the different ways of combining flat and syntactic structure knowledge to improve resolution performances.


However in our case, the event pronoun consists of only 14.7% of the total pronouns in OntoNotes.

we incorporate the instances from non-event pronouns to improve the precision of the classifier.


This is because balanced training set provides a better weighted positive/negative instances which implies a balanced positive/negative knowledge representation.

of that, we achieve a better balanced f-score.


We select sentences to fit the source content at run time, regardless to whether a pre-determined structural template exists or not.

the requirement on the structures of source articles becomes very flexible, enabling our system to work for arbitrary domain.


Segments from the same excerpts tend to be less redundant and more coherent.

candidates that share the same parent excerpts as segments in winner set are more favorable and rewarded by this term.


Instead, we only include the sentences in this segment that contain those keywords.

we propose two conditions: Thresholds in both conditions are not static but dependent on the highest score of all candidates in order to accommodate diversity in score range for different texts.


These two aspects can cause a trouble during a training phase because pairs of large parse trees that are similar to each other are disproportionately dominant.

the resulting models could act like nearest neighbor models (Collins and Duffy, 2001).


Miwa et al., (2009b) executed a set of combinatorial experiments by mixing multiple corpora and pre-sented their results.

it is not reasonable to compare our results with them due to the size discrepancy between training corpora.


However, the unpacking process will become problematic, because many of the proposed derivation trees during unpacking will be incorrect, leading to excessive computation times (in the order of minutes).

we chose to define robustness rules such, that the resulting chart items will be equally constrained as their daughters.


We then select appropriate word senses using web counts, and extract a set of patterns which connect these disambiguated terms.

we get a set of relation-specific target language patterns, effectively obtaining the desired relationship definition.


While the majority of studies focus on extracting pre-specified semantic relationships, several recent studies were done on the automated discovery of unspecified relationship types.

turney (2006) provided a pattern distance measure that allows a fully unsupervised measurement of relational similarity between two pairs of words on the same language.


However, the majority of CLIR studies pursue different targets.

one of the main CLIR goals is the retrieval of documents based on explicit queries, when the document language is not the query language (Volk and Buitelaar, 2002).


We then used the term pair selection algorithm described in Section 3.1 to select the most specific term pair for each of the 15 source language clusters and 10 pairs for each of the corresponding translated target language clusters.

for each of the 15 pattern clusters and for each of the 11 languages we produced a single source language term pair and up to 10 corresponding target language term pairs.


Comparison of relationships is a challenging task, since there are no relationship resources for most relationship types even in a single language, and certainly so for their translations across languages.

various studies define and split generic relationships differently even when describing relatively restricted relationship domains (e.g., relationships holding between parts of noun phrases (Nastase and Szpakowicz, 2003;Moldovan et al., 2004)).


Random stem generation Keeping the suffix fixed and randomly selecting a stem ties the generated form to the syntactic context, but changes the semantics.

these generated errors are firstly semantic errors (#1b), featuring stems inappropriate for the context, in addition to having some other morphological error.


Moreover, we do not study opinion holders (as they are known in the Web environment), so personal entities are not the aspect that we concentrate on.

we did not use the following features: semantic class agreement features, the gender agreement feature, and appositive feature.


As an example, "London, Kentucky" is distinguished from "London, Ontario", "London, Arkansas", "London (novel)", and "London".

other string equality features were used, such as whether names are equivalent after some transformation.


The combinations were cascaded to allow arbitrary feature conjunctions.

it is possible to end up with a feature kbtypeis-ORG AND high-TFIDF-score AND low-namesimilarity.


One issue with our KB was that it was derived from infoboxes in Wikipedia's Oct 2008 version which has both new entities, 12 and is missing entities.

13 we manually confirmed NIL answers and new answers for queries marked as NIL in the data.


the sequence labeling task becomes efficient and speedy using the DPLVM algorithm.


This ranking method, however, has not taken into account content relevance or the twitter account.

a large amount of pointless tweets inevitably flood the relevant tweets.